Perception of difficulty with Random Curve
## IDjoueur nom_du_joueur heure_connexion_joueur nom_du_jeu
## 1: 05ezh8lfl Sandrine Bruneau 12_20_2016_15h14m33s Logique2
## 2: 05ezh8lfl Sandrine Bruneau 12_20_2016_15h14m33s Logique2
## 3: 05ezh8lfl Sandrine Bruneau 12_20_2016_15h14m33s Logique2
## 4: 05ezh8lfl Sandrine Bruneau 12_20_2016_15h14m33s Logique2
## 5: 05ezh8lfl Sandrine Bruneau 12_20_2016_15h14m33s Logique2
## ---
## 5042: zv35u39vc Nadège BELLEC 12_20_2016_12h33m13s Motrice
## 5043: zv35u39vc Nadège BELLEC 12_20_2016_12h33m13s Motrice
## 5044: zv35u39vc Nadège BELLEC 12_20_2016_12h33m13s Motrice
## 5045: zv35u39vc Nadège BELLEC 12_20_2016_12h33m13s Motrice
## 5046: zv35u39vc Nadège BELLEC 12_20_2016_12h33m13s Motrice
## modeTest mise_first_1 action_de_jeu duree_tour_ms mise confiance
## 1: 0 1 2 43967 3 50
## 2: 0 1 3 27818 6 70
## 3: 0 1 4 18807 7 100
## 4: 0 1 5 24111 7 100
## 5: 0 1 6 30515 7 100
## ---
## 5042: 0 0 26 6363 7 100
## 5043: 0 0 27 6401 1 10
## 5044: 0 0 28 7363 2 30
## 5045: 0 0 29 6833 2 30
## 5046: 0 0 30 8730 1 20
## difficulty gameDiff near_miss moutons_sauves moutons_tues score
## 1: 0.41 5.00 0 10 0 10
## 2: 0.46 5.00 0 16 0 16
## 3: 0.17 2.00 0 23 0 23
## 4: 0.24 3.00 0 30 0 30
## 5: 0.45 5.00 0 37 0 37
## ---
## 5042: 0.20 3.60 17 60 59 1
## 5043: 0.87 8.96 -28 60 60 0
## 5044: 0.36 4.88 -10 62 60 2
## 5045: 0.55 6.40 -16 62 62 0
## 5046: 0.93 9.44 52 62 63 -1
## gagnant horodateur prenomNom age sexe
## 1: 1 12/20/2016 15:20:04 Sandrine Bruneau 46 1
## 2: 1 12/20/2016 15:20:04 Sandrine Bruneau 46 1
## 3: 1 12/20/2016 15:20:04 Sandrine Bruneau 46 1
## 4: 1 12/20/2016 15:20:04 Sandrine Bruneau 46 1
## 5: 1 12/20/2016 15:20:04 Sandrine Bruneau 46 1
## ---
## 5042: 0 12/20/2016 12:37:14 Nad<U+008A>ge BELLEC 38 1
## 5043: 0 12/20/2016 12:37:14 Nad<U+008A>ge BELLEC 38 1
## 5044: 1 12/20/2016 12:37:14 Nad<U+008A>ge BELLEC 38 1
## 5045: 0 12/20/2016 12:37:14 Nad<U+008A>ge BELLEC 38 1
## 5046: 0 12/20/2016 12:37:14 Nad<U+008A>ge BELLEC 38 1
## langueMaternelle niveauEtude
## 1: 1 7
## 2: 1 7
## 3: 1 7
## 4: 1 7
## 5: 1 7
## ---
## 5042: 1 4
## 5043: 1 4
## 5044: 1 4
## 5045: 1 4
## 5046: 1 4
## jeuxFav autoEffJoueur1
## 1: pacman_ NA
## 2: pacman_ NA
## 3: pacman_ NA
## 4: pacman_ NA
## 5: pacman_ NA
## ---
## 5042: TRI PEAK SOLITAIRE - BEST FRIEND - SUR ANDROID NA
## 5043: TRI PEAK SOLITAIRE - BEST FRIEND - SUR ANDROID NA
## 5044: TRI PEAK SOLITAIRE - BEST FRIEND - SUR ANDROID NA
## 5045: TRI PEAK SOLITAIRE - BEST FRIEND - SUR ANDROID NA
## 5046: TRI PEAK SOLITAIRE - BEST FRIEND - SUR ANDROID NA
## autoEffJoueur2 autoEffJoueur3 autoEffJoueur4 autoEffJoueur5
## 1: NA NA NA NA
## 2: NA NA NA NA
## 3: NA NA NA NA
## 4: NA NA NA NA
## 5: NA NA NA NA
## ---
## 5042: NA NA NA NA
## 5043: NA NA NA NA
## 5044: NA NA NA NA
## 5045: NA NA NA NA
## 5046: NA NA NA NA
## autoEffJoueur6 autoEffJoueur7 autoEffJoueur8 autoEffJoueur9
## 1: NA NA NA NA
## 2: NA NA NA NA
## 3: NA NA NA NA
## 4: NA NA NA NA
## 5: NA NA NA NA
## ---
## 5042: NA NA NA NA
## 5043: NA NA NA NA
## 5044: NA NA NA NA
## 5045: NA NA NA NA
## 5046: NA NA NA NA
## autoEffJoueur10 loterie1 loterie2 loterie3 loterie4 loterie5
## 1: NA 1 1 1 1 0
## 2: NA 1 1 1 1 0
## 3: NA 1 1 1 1 0
## 4: NA 1 1 1 1 0
## 5: NA 1 1 1 1 0
## ---
## 5042: NA 1 1 0 0 1
## 5043: NA 1 1 0 0 1
## 5044: NA 1 1 0 0 1
## 5045: NA 1 1 0 0 1
## 5046: NA 1 1 0 0 1
## loterie6 loterie7 loterie8 loterie9 loterie10 profilJoueur8
## 1: 0 1 1 1 1 0
## 2: 0 1 1 1 1 0
## 3: 0 1 1 1 1 0
## 4: 0 1 1 1 1 0
## 5: 0 1 1 1 1 0
## ---
## 5042: 1 1 1 1 1 0
## 5043: 1 1 1 1 1 0
## 5044: 1 1 1 1 1 0
## 5045: 1 1 1 1 1 0
## 5046: 1 1 1 1 1 0
## play.video.games play.board.games play.money.games self.eff
## 1: 0.4 0.2 0.8 NA
## 2: 0.4 0.2 0.8 NA
## 3: 0.4 0.2 0.8 NA
## 4: 0.4 0.2 0.8 NA
## 5: 0.4 0.2 0.8 NA
## ---
## 5042: 1.0 0.4 0.4 NA
## 5043: 1.0 0.4 0.4 NA
## 5044: 1.0 0.4 0.4 NA
## 5045: 1.0 0.4 0.4 NA
## 5046: 1.0 0.4 0.4 NA## Nombre de participants à l'expérimentation : 58## Nombre de participants se déclarant comme joueurs : 29## Nombre de femmes se déclarant comme joueuses : 3## Age médian des joueurs : 15Removing Outliers
## [1] "Outliers BET STANDARD DEVIATION: 3qq8dp8jk, 79pn8m6v8, e58u3sinl, urgv6o806"
## Empty data.table (0 rows) of 1 col: IDjoueur
## Empty data.table (0 rows) of 1 col: IDjoueur
## Empty data.table (0 rows) of 1 col: IDjoueur
## [1] "Outliers BET SAVED SHEEPS: "## Empty data.table (0 rows) of 1 col: IDjoueur## Empty data.table (0 rows) of 1 col: IDjoueur## Empty data.table (0 rows) of 1 col: IDjoueur## [1] "Total number of participants : 58"## [1] "Total number of outliers: 4"## [1] "- total number of outliers motor task: 2"## [1] "- total number of outliers perceptive task: 1"## [1] "- total number of outliers logical task: 1"## [1] "Total number of participants after removing outliers: 58"## [1] "- motor: 56"## [1] "- perceptive: 57"## [1] "- logical: 57"Modeling difficulty
Modeling objective difficulty for motor task
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: perdant ~ difficulty + timeNorm + (1 | IDjoueur)
## Data: DT
##
## AIC BIC logLik deviance df.resid
## 1953.7 1975.3 -972.8 1945.7 1620
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.1396 -0.7500 0.2888 0.7385 2.8481
##
## Random effects:
## Groups Name Variance Std.Dev.
## IDjoueur (Intercept) 0.5631 0.7504
## Number of obs: 1624, groups: IDjoueur, 56
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.0298 0.1873 -5.499 3.83e-08 ***
## difficulty 2.9618 0.2146 13.803 < 2e-16 ***
## timeNorm -0.5280 0.2020 -2.614 0.00895 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) dffclt
## difficulty -0.539
## timeNorm -0.571 -0.009## The result is correct only if all data used by the model has not changed since model was fitted.
## The result is correct only if all data used by the model has not changed since model was fitted.##
## Logique2 Motrice Sensoriel
## 0 1624 0
## [1] "Player levels from ranef:"
## (Intercept)
## Min. :-1.050110
## 1st Qu.:-0.438217
## Median :-0.118832
## Mean :-0.002364
## 3rd Qu.: 0.296005
## Max. : 1.658440
## [1] "Intercept: -1.03 3.8e-08 ***"
## [1] "Difficulty: 2.96 2.4e-43 ***"
## [1] "Time: -0.528 0.009 **"
## [1] "R2 fixed: 0.16"
## [1] "R2 mixed: 0.29"
## [1] "Cross Val: 0.68"
## [1] "AIC: 2000"## 0% 25% 50% 75% 100%
## -1.6584395 -0.2960052 0.1188317 0.4382172 1.0501105
## 0% 25% 50% 75% 100%
## -1.6584395 -0.2960052 0.1188317 0.4382172 1.0501105
## `geom_smooth()` using method = 'gam'
## `geom_smooth()` using method = 'loess'
## `geom_smooth()` using method = 'loess'
## `geom_smooth()` using method = 'loess'
Modeling objective difficulty for sensory task
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: perdant ~ difficulty + timeNorm + (1 | IDjoueur)
## Data: DT
##
## AIC BIC logLik deviance df.resid
## 1287.3 1308.9 -639.6 1279.3 1649
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -6.3813 -0.3588 0.1128 0.3561 6.6474
##
## Random effects:
## Groups Name Variance Std.Dev.
## IDjoueur (Intercept) 0.7479 0.8648
## Number of obs: 1653, groups: IDjoueur, 57
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -3.3420 0.2577 -12.967 <2e-16 ***
## difficulty 8.2722 0.4031 20.520 <2e-16 ***
## timeNorm -0.3164 0.2646 -1.196 0.232
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) dffclt
## difficulty -0.651
## timeNorm -0.516 -0.045## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control
## $checkConv, : Model failed to converge with max|grad| = 0.0968078 (tol =
## 0.001, component 1)## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, : Model is nearly unidentifiable: very large eigenvalue
## - Rescale variables?## The result is correct only if all data used by the model has not changed since model was fitted.## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, : Model failed to converge with max|grad| = 0.0968078 (tol = 0.001, component 1)
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, : Model is nearly unidentifiable: very large eigenvalue
## - Rescale variables?## The result is correct only if all data used by the model has not changed since model was fitted.##
## Logique2 Motrice Sensoriel
## 0 0 1653
## [1] "Player levels from ranef:"
## (Intercept)
## Min. :-1.6740621
## 1st Qu.:-0.4631746
## Median : 0.0733813
## Mean :-0.0008036
## 3rd Qu.: 0.4485664
## Max. : 1.5533202
## [1] "Intercept: -3.34 1.9e-38 ***"
## [1] "Difficulty: 8.27 1.4e-93 ***"
## [1] "Time: -0.316 0.23 :("
## [1] "R2 fixed: 0.3"
## [1] "R2 mixed: 0.46"
## [1] "Cross Val: 0.82"
## [1] "AIC: 1300"## 0% 25% 50% 75% 100%
## -1.55332020 -0.44856637 -0.07338126 0.46317464 1.67406213
## 0% 25% 50% 75% 100%
## -1.55332020 -0.44856637 -0.07338126 0.46317464 1.67406213
## `geom_smooth()` using method = 'gam'
## `geom_smooth()` using method = 'loess'
## `geom_smooth()` using method = 'loess'
## `geom_smooth()` using method = 'loess'
Modeling objective difficulty for logical task
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: perdant ~ difficulty + timeNorm + (1 | IDjoueur)
## Data: DT
##
## AIC BIC logLik deviance df.resid
## 1552.8 1574.4 -772.4 1544.8 1649
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -6.0811 -0.4934 -0.1180 0.4990 5.2065
##
## Random effects:
## Groups Name Variance Std.Dev.
## IDjoueur (Intercept) 1.53 1.237
## Number of obs: 1653, groups: IDjoueur, 57
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.7716 0.2500 -7.087 1.37e-12 ***
## difficulty 5.7158 0.3070 18.615 < 2e-16 ***
## timeNorm -2.1395 0.2486 -8.608 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) dffclt
## difficulty -0.487
## timeNorm -0.373 -0.253## The result is correct only if all data used by the model has not changed since model was fitted.
## The result is correct only if all data used by the model has not changed since model was fitted.##
## Logique2 Motrice Sensoriel
## 1653 0 0
## [1] "Player levels from ranef:"
## (Intercept)
## Min. :-1.8176657
## 1st Qu.:-0.7404031
## Median :-0.2056618
## Mean :-0.0000472
## 3rd Qu.: 0.7132065
## Max. : 3.1485721
## [1] "Intercept: -1.77 1.4e-12 ***"
## [1] "Difficulty: 5.72 2.4e-77 ***"
## [1] "Time: -2.14 7.5e-18 ***"
## [1] "R2 fixed: 0.39"
## [1] "R2 mixed: 0.58"
## [1] "Cross Val: 0.8"
## [1] "AIC: 1600"## 0% 25% 50% 75% 100%
## -3.1485721 -0.7132065 0.2056618 0.7404031 1.8176657
## 0% 25% 50% 75% 100%
## -3.1485721 -0.7132065 0.2056618 0.7404031 1.8176657
## `geom_smooth()` using method = 'gam'
## `geom_smooth()` using method = 'loess'
## `geom_smooth()` using method = 'loess'
## `geom_smooth()` using method = 'loess'
Influence of Player Profiles
Player profiles
Influence of Player Profiles
Objective level and player profile
Playing video games in general and level for each task
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 1.3815, p-value = 0.1671
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.1442117
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = -0.5689, p-value = 0.5694
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## -0.05907689
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = -0.36057, p-value = 0.7184
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## -0.0374431Playing board games in general and level for each task
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 0.86453, p-value = 0.3873
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.08913015
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = -0.19027, p-value = 0.8491
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## -0.01944679
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = -0.75722, p-value = 0.4489
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## -0.07770109Self efficacy and level for each task
## Warning in cor.test.default(Y, X, method = "kendall"): Cannot compute exact
## p-value with ties## Warning: Removed 28 rows containing missing values (geom_point).
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 0.17852, p-value = 0.8583
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.02429648## Warning in cor.test.default(Y, X, method = "kendall"): Cannot compute exact
## p-value with ties
## Warning in cor.test.default(Y, X, method = "kendall"): Removed 28 rows
## containing missing values (geom_point).
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 2.7713, p-value = 0.005584
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.3708505
##
## [1] "self.eff.on.level.s 0.37 0.0056 **"## Warning in cor.test.default(Y, X, method = "kendall"): Cannot compute exact
## p-value with ties
## Warning in cor.test.default(Y, X, method = "kendall"): Removed 28 rows
## containing missing values (geom_point).
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 0.69753, p-value = 0.4855
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.09334332Risk aversion and level for each task
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 1.5679, p-value = 0.1169
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.1554335
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 1.8306, p-value = 0.06716
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.1794643
##
## [1] "risk.av.on.level.s 0.18 0.067 ."
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 1.175, p-value = 0.24
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.1154221Age and level for each task
## Warning: Removed 1 rows containing missing values (geom_point).
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = -0.97478, p-value = 0.3297
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## -0.09369113## Warning: Removed 1 rows containing missing values (geom_point).
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 2.2707, p-value = 0.02317
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.2167271
##
## [1] "age.on.level.s 0.22 0.023 *"## Warning: Removed 1 rows containing missing values (geom_point).
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 1.1924, p-value = 0.2331
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.1137751Sex and level for each task
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = -2.1404, p-value = 0.03233
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## -0.2377395
##
## [1] "sexe.on.level.m -0.24 0.032 *"
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = -0.22007, p-value = 0.8258
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## -0.02422079
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = -0.20601, p-value = 0.8368
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## -0.0226739
##
## Wilcoxon rank sum test
##
## data: B and A
## W = 220, p-value = 0.03213
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -0.82775747 -0.05457213
## sample estimates:
## difference in location
## -0.4558716
##
## [1] "sexe.on.level.m.2 -0.46 0.032 * mean(A): 0.15 mean(B): -0.31"
##
## Wilcoxon rank sum test
##
## data: B and A
## W = 348, p-value = 0.8339
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -0.4740139 0.4341707
## sample estimates:
## difference in location
## -0.0477841
##
## Wilcoxon rank sum test
##
## data: B and A
## W = 339, p-value = 0.845
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -0.7708044 0.5990311
## sample estimates:
## difference in location
## -0.02530146CONFIDENCE SCALE APPROACH
For Bet approach, see the other file.
Influence of Objective difficulty on Subjective Difficulty
All tasks
## [1] "all"
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 0.0084 51 0.73 :(
## 2: 0.09375 0.0250 57 0.14 :(
## 3: 0.15625 -0.0130 57 0.44 :(
## 4: 0.21875 0.0430 58 0.14 :(
## 5: 0.28125 -0.0430 57 0.19 :(
## 6: 0.34375 0.0015 57 0.96 :(
## 7: 0.40625 0.0220 56 0.45 :(
## 8: 0.46875 -0.0220 57 0.56 :(
## 9: 0.53125 0.0044 55 0.98 :(
## 10: 0.59375 -0.0100 58 0.77 :(
## 11: 0.65625 -0.0640 58 0.035 *
## 12: 0.71875 -0.1100 58 2.5e-05 ***
## 13: 0.78125 -0.1500 56 3.7e-08 ***
## 14: 0.84375 -0.1900 56 3.9e-09 ***
## 15: 0.90625 -0.2000 57 4.9e-11 ***
## 16: 0.96875 -0.1700 57 4.9e-11 ***
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 51 0.73 :(
## 2: 57 0.14 :(
## 3: 57 0.44 :(
## 4: 58 0.14 :(
## 5: 57 0.19 :(
## 6: 57 0.96 :(
## 7: 56 0.45 :(
## 8: 57 0.56 :(
## 9: 55 0.98 :(
## 10: 58 0.77 :(
## 11: 58 0.035 *
## 12: 58 2.5e-05 ***
## 13: 56 3.7e-08 ***
## 14: 56 3.9e-09 ***
## 15: 57 4.9e-11 ***
## 16: 57 4.9e-11 ***
## [1] 56.6
## [1] 1.71
## [1] "good"## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 0.0130 36 0.83 :(
## 2: 0.09375 -0.0045 38 0.88 :(
## 3: 0.15625 -0.0670 44 0.2 :(
## 4: 0.21875 0.0130 42 0.74 :(
## 5: 0.28125 -0.0430 40 0.36 :(
## 6: 0.34375 0.0130 39 0.74 :(
## 7: 0.40625 0.0650 42 0.15 :(
## 8: 0.46875 0.0310 39 0.7 :(
## 9: 0.53125 -0.0220 40 0.91 :(
## 10: 0.59375 -0.0220 43 0.53 :(
## 11: 0.65625 -0.0780 36 0.045 *
## 12: 0.71875 -0.1500 39 0.00023 ***
## 13: 0.78125 -0.1800 38 0.00015 ***
## 14: 0.84375 -0.2400 27 1.7e-05 ***
## 15: 0.90625 -0.1900 31 1.1e-06 ***
## 16: 0.96875 -0.1500 20 7.5e-05 ***
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 36 0.83 :(
## 2: 38 0.88 :(
## 3: 44 0.2 :(
## 4: 42 0.74 :(
## 5: 40 0.36 :(
## 6: 39 0.74 :(
## 7: 42 0.15 :(
## 8: 39 0.7 :(
## 9: 40 0.91 :(
## 10: 43 0.53 :(
## 11: 36 0.045 *
## 12: 39 0.00023 ***
## 13: 38 0.00015 ***
## 14: 27 1.7e-05 ***
## 15: 31 1.1e-06 ***
## 16: 20 7.5e-05 ***
## [1] 37.1
## [1] 6.29
## [1] "medium"## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 -0.031 29 0.18 :(
## 2: 0.09375 0.040 33 0.089 .
## 3: 0.15625 0.058 31 0.61 :(
## 4: 0.21875 0.013 38 0.98 :(
## 5: 0.28125 -0.067 36 0.27 :(
## 6: 0.34375 -0.058 36 0.34 :(
## 7: 0.40625 -0.025 35 0.56 :(
## 8: 0.46875 -0.040 35 0.38 :(
## 9: 0.53125 0.088 36 0.072 .
## 10: 0.59375 0.073 35 0.19 :(
## 11: 0.65625 -0.067 36 0.2 :(
## 12: 0.71875 -0.076 38 0.1 :(
## 13: 0.78125 -0.067 38 0.018 *
## 14: 0.84375 -0.130 37 3.4e-05 ***
## 15: 0.90625 -0.190 34 3.6e-07 ***
## 16: 0.96875 -0.180 32 8.2e-07 ***
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 29 0.18 :(
## 2: 33 0.089 .
## 3: 31 0.61 :(
## 4: 38 0.98 :(
## 5: 36 0.27 :(
## 6: 36 0.34 :(
## 7: 35 0.56 :(
## 8: 35 0.38 :(
## 9: 36 0.072 .
## 10: 35 0.19 :(
## 11: 36 0.2 :(
## 12: 38 0.1 :(
## 13: 38 0.018 *
## 14: 37 3.4e-05 ***
## 15: 34 3.6e-07 ***
## 16: 32 8.2e-07 ***
## [1] 34.9
## [1] 2.59
## [1] "bad"## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 NA 1 NA
## 2: 0.09375 -0.0220 9 0.72 :(
## 3: 0.15625 -0.0041 12 1 :(
## 4: 0.21875 -0.0045 13 0.52 :(
## 5: 0.28125 0.1000 12 0.32 :(
## 6: 0.34375 0.1400 11 0.12 :(
## 7: 0.40625 0.0760 14 0.29 :(
## 8: 0.46875 -0.0760 17 0.42 :(
## 9: 0.53125 -0.1000 15 0.27 :(
## 10: 0.59375 -0.1100 16 0.17 :(
## 11: 0.65625 -0.1000 17 0.11 :(
## 12: 0.71875 -0.1100 16 0.052 .
## 13: 0.78125 -0.1700 17 0.0024 **
## 14: 0.84375 -0.1800 20 0.0042 **
## 15: 0.90625 -0.1600 20 9.4e-05 ***
## 16: 0.96875 -0.2400 20 9.4e-05 ***
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 9 0.72 :(
## 2: 12 1 :(
## 3: 13 0.52 :(
## 4: 12 0.32 :(
## 5: 11 0.12 :(
## 6: 14 0.29 :(
## 7: 17 0.42 :(
## 8: 15 0.27 :(
## 9: 16 0.17 :(
## 10: 17 0.11 :(
## 11: 16 0.052 .
## 12: 17 0.0024 **
## 13: 20 0.0042 **
## 14: 20 9.4e-05 ***
## 15: 20 9.4e-05 ***
## [1] 15.3
## [1] 3.41## Warning: Removed 1 rows containing missing values (geom_point).## Warning: Removed 1 rows containing missing values (geom_errorbar).
Motor task
## [1] "all"## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 NA 0 NA
## 2: 0.09375 -0.094 8 0.21 :(
## 3: 0.15625 -0.099 26 0.015 *
## 4: 0.21875 -0.076 40 0.0065 **
## 5: 0.28125 -0.067 45 0.055 .
## 6: 0.34375 -0.058 47 0.21 :(
## 7: 0.40625 -0.013 49 0.8 :(
## 8: 0.46875 0.031 49 0.73 :(
## 9: 0.53125 0.076 51 0.15 :(
## 10: 0.59375 0.025 51 0.55 :(
## 11: 0.65625 -0.013 53 0.45 :(
## 12: 0.71875 -0.052 51 0.079 .
## 13: 0.78125 -0.067 44 0.029 *
## 14: 0.84375 -0.094 27 0.0073 **
## 15: 0.90625 -0.078 14 0.00076 ***
## 16: 0.96875 -0.110 6 0.034 *
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 8 0.21 :(
## 2: 26 0.015 *
## 3: 40 0.0065 **
## 4: 45 0.055 .
## 5: 47 0.21 :(
## 6: 49 0.8 :(
## 7: 49 0.73 :(
## 8: 51 0.15 :(
## 9: 51 0.55 :(
## 10: 53 0.45 :(
## 11: 51 0.079 .
## 12: 44 0.029 *
## 13: 27 0.0073 **
## 14: 14 0.00076 ***
## 15: 6 0.034 *
## [1] 37.4
## [1] 16.7## Warning: Removed 1 rows containing missing values (geom_point).## Warning: Removed 1 rows containing missing values (geom_errorbar).
## [1] "good"## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 NA 0 NA
## 2: 0.09375 -0.0940 8 0.21 :(
## 3: 0.15625 -0.1200 24 0.005 **
## 4: 0.21875 -0.0760 26 0.031 *
## 5: 0.28125 -0.0670 25 0.12 :(
## 6: 0.34375 0.0130 26 0.8 :(
## 7: 0.40625 0.0320 25 0.67 :(
## 8: 0.46875 0.0880 24 0.14 :(
## 9: 0.53125 0.0760 23 0.21 :(
## 10: 0.59375 0.0970 24 0.038 *
## 11: 0.65625 0.0081 25 0.94 :(
## 12: 0.71875 -0.0470 22 0.078 .
## 13: 0.78125 -0.1000 15 0.26 :(
## 14: 0.84375 NA 0 NA
## 15: 0.90625 NA 0 NA
## 16: 0.96875 NA 0 NA
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 8 0.21 :(
## 2: 24 0.005 **
## 3: 26 0.031 *
## 4: 25 0.12 :(
## 5: 26 0.8 :(
## 6: 25 0.67 :(
## 7: 24 0.14 :(
## 8: 23 0.21 :(
## 9: 24 0.038 *
## 10: 25 0.94 :(
## 11: 22 0.078 .
## 12: 15 0.26 :(
## [1] 22.2
## [1] 5.36## Warning: Removed 4 rows containing missing values (geom_point).## Warning: Removed 4 rows containing missing values (geom_errorbar).
## [1] "medium"## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 NA 0 NA
## 2: 0.09375 NA 0 NA
## 3: 0.15625 0.2000 2 1 :(
## 4: 0.21875 -0.2200 14 0.15 :(
## 5: 0.28125 -0.0990 20 0.38 :(
## 6: 0.34375 -0.1600 20 0.08 .
## 7: 0.40625 -0.0490 22 0.31 :(
## 8: 0.46875 -0.0160 21 0.63 :(
## 9: 0.53125 0.1400 21 0.0048 **
## 10: 0.59375 0.0130 21 0.86 :(
## 11: 0.65625 -0.0130 21 0.94 :(
## 12: 0.71875 0.0430 22 0.43 :(
## 13: 0.78125 -0.0099 21 0.75 :(
## 14: 0.84375 -0.0940 19 0.017 *
## 15: 0.90625 NA 6 NA
## 16: 0.96875 NA 0 NA
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 2 1 :(
## 2: 14 0.15 :(
## 3: 20 0.38 :(
## 4: 20 0.08 .
## 5: 22 0.31 :(
## 6: 21 0.63 :(
## 7: 21 0.0048 **
## 8: 21 0.86 :(
## 9: 21 0.94 :(
## 10: 22 0.43 :(
## 11: 21 0.75 :(
## 12: 19 0.017 *
## [1] 18.7
## [1] 5.66## Warning: Removed 4 rows containing missing values (geom_point).## Warning: Removed 4 rows containing missing values (geom_errorbar).
## [1] "bad"## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 NA 0 NA
## 2: 0.09375 NA 0 NA
## 3: 0.15625 NA 0 NA
## 4: 0.21875 NA 0 NA
## 5: 0.28125 NA 0 NA
## 6: 0.34375 NA 1 NA
## 7: 0.40625 -0.049 2 1 :(
## 8: 0.46875 -0.180 4 0.58 :(
## 9: 0.53125 -0.400 7 0.071 .
## 10: 0.59375 -0.290 6 0.14 :(
## 11: 0.65625 -0.230 7 0.16 :(
## 12: 0.71875 -0.250 7 0.047 *
## 13: 0.78125 -0.180 8 0.023 *
## 14: 0.84375 -0.110 8 0.29 :(
## 15: 0.90625 -0.110 8 0.013 *
## 16: 0.96875 -0.110 6 0.034 *
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 2 1 :(
## 2: 4 0.58 :(
## 3: 7 0.071 .
## 4: 6 0.14 :(
## 5: 7 0.16 :(
## 6: 7 0.047 *
## 7: 8 0.023 *
## 8: 8 0.29 :(
## 9: 8 0.013 *
## 10: 6 0.034 *
## [1] 6.3
## [1] 1.95## Warning: Removed 6 rows containing missing values (geom_point).## Warning: Removed 6 rows containing missing values (geom_errorbar).
Sensory task
## [1] "all"## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 -0.031 45 0.025 *
## 2: 0.09375 -0.094 54 0.01 *
## 3: 0.15625 -0.085 50 0.027 *
## 4: 0.21875 -0.040 41 0.18 :(
## 5: 0.28125 -0.067 40 0.35 :(
## 6: 0.34375 -0.094 38 0.15 :(
## 7: 0.40625 0.022 37 0.69 :(
## 8: 0.46875 -0.110 37 0.029 *
## 9: 0.53125 -0.140 30 0.045 *
## 10: 0.59375 -0.190 37 0.0051 **
## 11: 0.65625 -0.160 36 0.016 *
## 12: 0.71875 -0.180 35 0.002 **
## 13: 0.78125 -0.170 38 0.00063 ***
## 14: 0.84375 -0.140 46 5e-05 ***
## 15: 0.90625 -0.170 54 1.2e-10 ***
## 16: 0.96875 -0.140 57 4.3e-11 ***
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 45 0.025 *
## 2: 54 0.01 *
## 3: 50 0.027 *
## 4: 41 0.18 :(
## 5: 40 0.35 :(
## 6: 38 0.15 :(
## 7: 37 0.69 :(
## 8: 37 0.029 *
## 9: 30 0.045 *
## 10: 37 0.0051 **
## 11: 36 0.016 *
## 12: 35 0.002 **
## 13: 38 0.00063 ***
## 14: 46 5e-05 ***
## 15: 54 1.2e-10 ***
## 16: 57 4.3e-11 ***
## [1] 42.2
## [1] 7.93
## [1] "good"## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 -0.0310 20 0.57 :(
## 2: 0.09375 -0.0940 19 0.0012 **
## 3: 0.15625 -0.1600 18 0.04 *
## 4: 0.21875 -0.0045 11 0.39 :(
## 5: 0.28125 -0.0670 17 0.44 :(
## 6: 0.34375 -0.2000 12 0.037 *
## 7: 0.40625 -0.0490 13 0.48 :(
## 8: 0.46875 -0.2500 15 0.011 *
## 9: 0.53125 -0.3000 11 0.027 *
## 10: 0.59375 -0.3100 14 0.0096 **
## 11: 0.65625 -0.2300 14 0.038 *
## 12: 0.71875 -0.3600 12 0.0025 **
## 13: 0.78125 -0.3200 12 0.011 *
## 14: 0.84375 -0.2200 14 0.0082 **
## 15: 0.90625 -0.1600 19 0.00013 ***
## 16: 0.96875 -0.1500 20 8.1e-05 ***
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 20 0.57 :(
## 2: 19 0.0012 **
## 3: 18 0.04 *
## 4: 11 0.39 :(
## 5: 17 0.44 :(
## 6: 12 0.037 *
## 7: 13 0.48 :(
## 8: 15 0.011 *
## 9: 11 0.027 *
## 10: 14 0.0096 **
## 11: 14 0.038 *
## 12: 12 0.0025 **
## 13: 12 0.011 *
## 14: 14 0.0082 **
## 15: 19 0.00013 ***
## 16: 20 8.1e-05 ***
## [1] 15.1
## [1] 3.28
## [1] "medium"## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 -0.031 25 0.0082 **
## 2: 0.09375 -0.094 27 0.31 :(
## 3: 0.15625 -0.120 22 0.041 *
## 4: 0.21875 -0.040 22 0.24 :(
## 5: 0.28125 -0.067 16 0.77 :(
## 6: 0.34375 -0.022 21 1 :(
## 7: 0.40625 0.022 19 0.51 :(
## 8: 0.46875 -0.110 17 0.25 :(
## 9: 0.53125 -0.100 15 0.44 :(
## 10: 0.59375 -0.150 16 0.31 :(
## 11: 0.65625 -0.160 17 0.17 :(
## 12: 0.71875 -0.076 16 0.15 :(
## 13: 0.78125 -0.067 21 0.11 :(
## 14: 0.84375 -0.130 24 0.0066 **
## 15: 0.90625 -0.190 27 4.7e-06 ***
## 16: 0.96875 -0.140 27 5.5e-06 ***
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 25 0.0082 **
## 2: 27 0.31 :(
## 3: 22 0.041 *
## 4: 22 0.24 :(
## 5: 16 0.77 :(
## 6: 21 1 :(
## 7: 19 0.51 :(
## 8: 17 0.25 :(
## 9: 15 0.44 :(
## 10: 16 0.31 :(
## 11: 17 0.17 :(
## 12: 16 0.15 :(
## 13: 21 0.11 :(
## 14: 24 0.0066 **
## 15: 27 4.7e-06 ***
## 16: 27 5.5e-06 ***
## [1] 20.8
## [1] 4.33
## [1] "bad"## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 NA 0 NA
## 2: 0.09375 -0.0220 8 0.94 :(
## 3: 0.15625 0.0220 10 0.61 :(
## 4: 0.21875 0.0260 8 1 :(
## 5: 0.28125 -0.0670 7 0.93 :(
## 6: 0.34375 -0.0220 5 0.78 :(
## 7: 0.40625 0.1200 5 0.44 :(
## 8: 0.46875 0.2100 5 0.19 :(
## 9: 0.53125 0.0760 4 0.88 :(
## 10: 0.59375 -0.1700 7 0.55 :(
## 11: 0.65625 -0.0130 5 0.78 :(
## 12: 0.71875 0.0063 7 1 :(
## 13: 0.78125 -0.2100 5 0.19 :(
## 14: 0.84375 -0.0580 8 0.29 :(
## 15: 0.90625 -0.1700 8 0.014 *
## 16: 0.96875 -0.1200 10 0.0059 **
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 8 0.94 :(
## 2: 10 0.61 :(
## 3: 8 1 :(
## 4: 7 0.93 :(
## 5: 5 0.78 :(
## 6: 5 0.44 :(
## 7: 5 0.19 :(
## 8: 4 0.88 :(
## 9: 7 0.55 :(
## 10: 5 0.78 :(
## 11: 7 1 :(
## 12: 5 0.19 :(
## 13: 8 0.29 :(
## 14: 8 0.014 *
## 15: 10 0.0059 **
## [1] 6.8
## [1] 1.9## Warning: Removed 1 rows containing missing values (geom_point).## Warning: Removed 1 rows containing missing values (geom_errorbar).
Logical task
## [1] "all"## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 0.0130 40 0.95 :(
## 2: 0.09375 0.1200 44 0.015 *
## 3: 0.15625 0.0580 46 0.19 :(
## 4: 0.21875 0.1900 48 0.0019 **
## 5: 0.28125 0.1100 36 0.2 :(
## 6: 0.34375 0.0850 44 0.078 .
## 7: 0.40625 0.0560 43 0.11 :(
## 8: 0.46875 -0.0045 42 0.9 :(
## 9: 0.53125 -0.0310 42 0.45 :(
## 10: 0.59375 -0.0220 46 0.94 :(
## 11: 0.65625 -0.0850 40 0.11 :(
## 12: 0.71875 -0.1500 44 0.016 *
## 13: 0.78125 -0.1400 47 0.00089 ***
## 14: 0.84375 -0.2700 48 1.5e-08 ***
## 15: 0.90625 -0.2400 43 1.1e-08 ***
## 16: 0.96875 -0.3000 29 2.7e-06 ***
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 40 0.95 :(
## 2: 44 0.015 *
## 3: 46 0.19 :(
## 4: 48 0.0019 **
## 5: 36 0.2 :(
## 6: 44 0.078 .
## 7: 43 0.11 :(
## 8: 42 0.9 :(
## 9: 42 0.45 :(
## 10: 46 0.94 :(
## 11: 40 0.11 :(
## 12: 44 0.016 *
## 13: 47 0.00089 ***
## 14: 48 1.5e-08 ***
## 15: 43 1.1e-08 ***
## 16: 29 2.7e-06 ***
## [1] 42.6
## [1] 4.83
## [1] "good"## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 0.0130 28 0.75 :(
## 2: 0.09375 0.0490 28 0.33 :(
## 3: 0.15625 0.0580 27 0.69 :(
## 4: 0.21875 0.1900 26 0.0095 **
## 5: 0.28125 -0.0074 18 1 :(
## 6: 0.34375 0.0340 24 0.56 :(
## 7: 0.40625 0.1700 22 0.034 *
## 8: 0.46875 0.1000 21 0.25 :(
## 9: 0.53125 -0.0310 23 0.66 :(
## 10: 0.59375 -0.0760 24 0.15 :(
## 11: 0.65625 -0.1200 17 0.042 *
## 12: 0.71875 -0.1100 21 0.07 .
## 13: 0.78125 -0.1700 23 0.019 *
## 14: 0.84375 -0.2700 21 0.00015 ***
## 15: 0.90625 -0.2600 16 0.00046 ***
## 16: 0.96875 NA 1 NA
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 28 0.75 :(
## 2: 28 0.33 :(
## 3: 27 0.69 :(
## 4: 26 0.0095 **
## 5: 18 1 :(
## 6: 24 0.56 :(
## 7: 22 0.034 *
## 8: 21 0.25 :(
## 9: 23 0.66 :(
## 10: 24 0.15 :(
## 11: 17 0.042 *
## 12: 21 0.07 .
## 13: 23 0.019 *
## 14: 21 0.00015 ***
## 15: 16 0.00046 ***
## [1] 22.6
## [1] 3.76## Warning: Removed 1 rows containing missing values (geom_point).## Warning: Removed 1 rows containing missing values (geom_errorbar).
## [1] "medium"## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 -0.031 11 0.82 :(
## 2: 0.09375 0.330 15 0.011 *
## 3: 0.15625 0.240 16 0.052 .
## 4: 0.21875 0.210 16 0.027 *
## 5: 0.28125 0.100 12 0.25 :(
## 6: 0.34375 0.085 12 0.29 :(
## 7: 0.40625 -0.085 13 0.67 :(
## 8: 0.46875 -0.040 12 0.55 :(
## 9: 0.53125 -0.016 11 1 :(
## 10: 0.59375 0.190 13 0.023 *
## 11: 0.65625 -0.085 12 0.56 :(
## 12: 0.71875 -0.290 15 0.062 .
## 13: 0.78125 -0.100 15 0.1 :(
## 14: 0.84375 -0.220 17 0.00091 ***
## 15: 0.90625 -0.280 16 0.00048 ***
## 16: 0.96875 -0.330 16 0.00048 ***
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 11 0.82 :(
## 2: 15 0.011 *
## 3: 16 0.052 .
## 4: 16 0.027 *
## 5: 12 0.25 :(
## 6: 12 0.29 :(
## 7: 13 0.67 :(
## 8: 12 0.55 :(
## 9: 11 1 :(
## 10: 13 0.023 *
## 11: 12 0.56 :(
## 12: 15 0.062 .
## 13: 15 0.1 :(
## 14: 17 0.00091 ***
## 15: 16 0.00048 ***
## 16: 16 0.00048 ***
## [1] 13.9
## [1] 2.06
## [1] "bad"## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 NA 1 NA
## 2: 0.09375 NA 1 NA
## 3: 0.15625 NA 3 NA
## 4: 0.21875 -0.040 6 0.83 :(
## 5: 0.28125 0.150 6 0.14 :(
## 6: 0.34375 0.320 8 0.079 .
## 7: 0.40625 0.022 8 0.44 :(
## 8: 0.46875 -0.040 9 0.34 :(
## 9: 0.53125 -0.100 8 0.44 :(
## 10: 0.59375 -0.170 9 0.41 :(
## 11: 0.65625 0.022 11 0.96 :(
## 12: 0.71875 0.001 8 1 :(
## 13: 0.78125 -0.140 9 0.12 :(
## 14: 0.84375 -0.340 10 0.014 *
## 15: 0.90625 -0.180 11 0.0038 **
## 16: 0.96875 -0.320 12 0.00049 ***
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 6 0.83 :(
## 2: 6 0.14 :(
## 3: 8 0.079 .
## 4: 8 0.44 :(
## 5: 9 0.34 :(
## 6: 8 0.44 :(
## 7: 9 0.41 :(
## 8: 11 0.96 :(
## 9: 8 1 :(
## 10: 9 0.12 :(
## 11: 10 0.014 *
## 12: 11 0.0038 **
## 13: 12 0.00049 ***
## [1] 8.85
## [1] 1.82## Warning: Removed 3 rows containing missing values (geom_point).## Warning: Removed 3 rows containing missing values (geom_errorbar).
Influence of Playtime on Subjective Difficulty Error
For all groups, motor, sensitive and logical
##
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff,
## data = DTM)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.70319 -0.16766 0.00799 0.17682 0.64502
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.17379 0.01995 8.711 <2e-16 ***
## timeNorm 0.00431 0.02101 0.205 0.837
## obj.diff -0.37273 0.02619 -14.234 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.05419176)
##
## Null deviance: 99.193 on 1623 degrees of freedom
## Residual deviance: 87.845 on 1621 degrees of freedom
## AIC: -120.62
##
## Number of Fisher Scoring iterations: 2
##
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff,
## data = DTS)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.77891 -0.19889 -0.04038 0.24162 0.77963
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.10459 0.01719 6.086 1.44e-09 ***
## timeNorm 0.04073 0.02300 1.771 0.0768 .
## obj.diff -0.36296 0.01778 -20.408 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.06800604)
##
## Null deviance: 140.80 on 1652 degrees of freedom
## Residual deviance: 112.21 on 1650 degrees of freedom
## AIC: 252.48
##
## Number of Fisher Scoring iterations: 2
##
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff,
## data = DTL)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.72757 -0.18824 -0.01492 0.18450 0.70120
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.22493 0.01907 11.80 <2e-16 ***
## timeNorm 0.07505 0.02360 3.18 0.0015 **
## obj.diff -0.55871 0.02005 -27.86 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.06619562)
##
## Null deviance: 169.02 on 1652 degrees of freedom
## Residual deviance: 109.22 on 1650 degrees of freedom
## AIC: 207.88
##
## Number of Fisher Scoring iterations: 2## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.5348214 0.6008109 -0.0627956484 112 0.021 *
## 2: 4.5 0.5291667 0.5714407 -0.0363491910 168 0.071 .
## 3: 7.5 0.5071429 0.5416953 -0.0317522384 168 0.12 :(
## 4: 10.5 0.5339286 0.5401276 0.0027661467 168 0.89 :(
## 5: 13.5 0.5071429 0.5174551 -0.0066784780 168 0.74 :(
## 6: 16.5 0.5232143 0.5305272 -0.0054376495 168 0.78 :(
## 7: 19.5 0.4976190 0.5315528 -0.0349803686 168 0.062 .
## 8: 22.5 0.4779762 0.4897264 -0.0103383643 168 0.64 :(
## 9: 25.5 0.4797619 0.4805683 0.0009212402 168 0.95 :(
## 10: 28.5 0.4642857 0.4572889 0.0071690193 168 0.72 :(
## time error.diff shapes
## 1: 1.5 -0.0627956484 24
## 2: 4.5 -0.0363491910 16
## 3: 7.5 -0.0317522384 16
## 4: 10.5 0.0027661467 16
## 5: 13.5 -0.0066784780 16
## 6: 16.5 -0.0054376495 16
## 7: 19.5 -0.0349803686 16
## 8: 22.5 -0.0103383643 16
## 9: 25.5 0.0009212402 16
## 10: 28.5 0.0071690193 16
## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.4675439 0.5915162 -0.13547480 114 1.9e-05 ***
## 2: 4.5 0.5064327 0.6086245 -0.08890313 171 2.4e-06 ***
## 3: 7.5 0.4643275 0.5299704 -0.06487600 171 0.0023 **
## 4: 10.5 0.5116959 0.5811872 -0.06875274 171 0.00088 ***
## 5: 13.5 0.4719298 0.5608340 -0.08034513 171 1.4e-05 ***
## 6: 16.5 0.4298246 0.5253020 -0.10455036 171 7.9e-06 ***
## 7: 19.5 0.4847953 0.5627322 -0.06849638 171 0.00024 ***
## 8: 22.5 0.4947368 0.5603749 -0.05563539 171 0.0035 **
## 9: 25.5 0.5356725 0.5815067 -0.03269431 171 0.074 .
## 10: 28.5 0.4970760 0.5691664 -0.07055827 171 0.0019 **
## time error.diff shapes
## 1: 1.5 -0.13547480 24
## 2: 4.5 -0.08890313 24
## 3: 7.5 -0.06487600 24
## 4: 10.5 -0.06875274 24
## 5: 13.5 -0.08034513 24
## 6: 16.5 -0.10455036 24
## 7: 19.5 -0.06849638 24
## 8: 22.5 -0.05563539 24
## 9: 25.5 -0.03269431 16
## 10: 28.5 -0.07055827 24
## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.4412281 0.6044431 -0.170878154 114 1.2e-06 ***
## 2: 4.5 0.5081871 0.6442014 -0.150571723 171 5.3e-08 ***
## 3: 7.5 0.5093567 0.5633809 -0.064995099 171 0.011 *
## 4: 10.5 0.5204678 0.5330653 -0.016026700 171 0.51 :(
## 5: 13.5 0.5157895 0.5198918 -0.009673167 171 0.71 :(
## 6: 16.5 0.5093567 0.4996879 0.003991574 171 0.88 :(
## 7: 19.5 0.4614035 0.4399282 0.012356963 171 0.58 :(
## 8: 22.5 0.4280702 0.4071581 0.015645798 171 0.51 :(
## 9: 25.5 0.4614035 0.3861396 0.082671444 171 0.0019 **
## 10: 28.5 0.4485380 0.3521331 0.085368787 171 0.001 **
## time error.diff shapes
## 1: 1.5 -0.170878154 24
## 2: 4.5 -0.150571723 24
## 3: 7.5 -0.064995099 24
## 4: 10.5 -0.016026700 16
## 5: 13.5 -0.009673167 16
## 6: 16.5 0.003991574 16
## 7: 19.5 0.012356963 16
## 8: 22.5 0.015645798 16
## 9: 25.5 0.082671444 24
## 10: 28.5 0.085368787 24
For all taks, per group
##
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff,
## data = DTAll[niveau.group == "bad"])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.74631 -0.17417 -0.06454 0.23463 0.56912
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.28522 0.03182 8.965 < 2e-16 ***
## timeNorm 0.08444 0.03049 2.770 0.00573 **
## obj.diff -0.61929 0.03245 -19.083 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.06163681)
##
## Null deviance: 77.771 on 869 degrees of freedom
## Residual deviance: 53.439 on 867 degrees of freedom
## AIC: 49.696
##
## Number of Fisher Scoring iterations: 2
##
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff,
## data = DTAll[niveau.group == "medium"])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.73122 -0.20517 0.00624 0.21924 0.73661
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.19286 0.01780 10.834 <2e-16 ***
## timeNorm 0.04068 0.02127 1.913 0.056 .
## obj.diff -0.45393 0.01979 -22.933 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.06630014)
##
## Null deviance: 163.11 on 1913 degrees of freedom
## Residual deviance: 126.70 on 1911 degrees of freedom
## AIC: 242.93
##
## Number of Fisher Scoring iterations: 2
##
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff,
## data = DTAll[niveau.group == "good"])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.74697 -0.19411 -0.00423 0.20394 0.71090
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.14668 0.01508 9.724 <2e-16 ***
## timeNorm 0.04018 0.01957 2.053 0.0402 *
## obj.diff -0.39929 0.01877 -21.269 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.06192699)
##
## Null deviance: 162.90 on 2145 degrees of freedom
## Residual deviance: 132.71 on 2143 degrees of freedom
## AIC: 125.34
##
## Number of Fisher Scoring iterations: 2## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.5550000 0.7911337 -0.24615978 60 8.9e-08 ***
## 2: 4.5 0.5711111 0.7789270 -0.22600865 90 6.4e-08 ***
## 3: 7.5 0.6122222 0.7692393 -0.17328232 90 2.6e-06 ***
## 4: 10.5 0.6355556 0.7341570 -0.10520584 90 0.0027 **
## 5: 13.5 0.6277778 0.7653763 -0.16821149 90 1.2e-05 ***
## 6: 16.5 0.6155556 0.7325880 -0.13596399 90 0.00023 ***
## 7: 19.5 0.6311111 0.7153889 -0.09339359 90 0.0017 **
## 8: 22.5 0.6188889 0.7245870 -0.10874983 90 0.0029 **
## 9: 25.5 0.6011111 0.6862825 -0.08012592 90 0.027 *
## 10: 28.5 0.6100000 0.6602684 -0.04668910 90 0.17 :(
## time error.diff shapes
## 1: 1.5 -0.24615978 24
## 2: 4.5 -0.22600865 24
## 3: 7.5 -0.17328232 24
## 4: 10.5 -0.10520584 24
## 5: 13.5 -0.16821149 24
## 6: 16.5 -0.13596399 24
## 7: 19.5 -0.09339359 24
## 8: 22.5 -0.10874983 24
## 9: 25.5 -0.08012592 24
## 10: 28.5 -0.04668910 16
## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.5053030 0.6039107 -0.099983959 132 0.00058 ***
## 2: 4.5 0.5661616 0.6746817 -0.107388316 198 4.2e-07 ***
## 3: 7.5 0.5090909 0.5371689 -0.034806432 198 0.099 .
## 4: 10.5 0.5434343 0.5804839 -0.035366367 198 0.12 :(
## 5: 13.5 0.5287879 0.5665343 -0.035968443 198 0.062 .
## 6: 16.5 0.5156566 0.5499096 -0.039303401 198 0.065 .
## 7: 19.5 0.4959596 0.5544583 -0.060120358 198 0.0038 **
## 8: 22.5 0.4868687 0.5086071 -0.026774034 198 0.2 :(
## 9: 25.5 0.5318182 0.5182748 0.009712085 198 0.68 :(
## 10: 28.5 0.5070707 0.5052319 -0.006835793 198 0.73 :(
## time error.diff shapes
## 1: 1.5 -0.099983959 24
## 2: 4.5 -0.107388316 24
## 3: 7.5 -0.034806432 16
## 4: 10.5 -0.035366367 16
## 5: 13.5 -0.035968443 16
## 6: 16.5 -0.039303401 16
## 7: 19.5 -0.060120358 24
## 8: 22.5 -0.026774034 16
## 9: 25.5 0.009712085 16
## 10: 28.5 -0.006835793 16
## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.4290541 0.5165266 -0.083246216 148 0.0015 **
## 2: 4.5 0.4454955 0.4799317 -0.033757771 222 0.081 .
## 3: 7.5 0.4315315 0.4611572 -0.026799425 222 0.15 :(
## 4: 10.5 0.4567568 0.4516607 0.008265497 222 0.66 :(
## 5: 13.5 0.4184685 0.4084635 0.016416425 222 0.44 :(
## 6: 16.5 0.4099099 0.4035442 0.005741364 222 0.79 :(
## 7: 19.5 0.4072072 0.3900362 0.012366817 222 0.52 :(
## 8: 22.5 0.3873874 0.3684916 0.017586402 222 0.32 :(
## 9: 25.5 0.4130631 0.3685548 0.043485459 222 0.013 *
## 10: 28.5 0.3801802 0.3374175 0.035295984 222 0.067 .
## time error.diff shapes
## 1: 1.5 -0.083246216 24
## 2: 4.5 -0.033757771 16
## 3: 7.5 -0.026799425 16
## 4: 10.5 0.008265497 16
## 5: 13.5 0.016416425 16
## 6: 16.5 0.005741364 16
## 7: 19.5 0.012366817 16
## 8: 22.5 0.017586402 16
## 9: 25.5 0.043485459 24
## 10: 28.5 0.035295984 16
Per group, motor task
##
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff,
## data = DTM[niveau.group == "bad"])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.65062 -0.16552 -0.07705 0.21881 0.38387
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.29158 0.07860 3.710 0.00026 ***
## timeNorm 0.04078 0.04734 0.861 0.38990
## obj.diff -0.58583 0.08967 -6.533 4.12e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.03968196)
##
## Null deviance: 10.9054 on 231 degrees of freedom
## Residual deviance: 9.0872 on 229 degrees of freedom
## AIC: -85.263
##
## Number of Fisher Scoring iterations: 2## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.6250000 0.8544830 -0.23067342 16 0.0013 **
## 2: 4.5 0.6375000 0.7995145 -0.16957820 24 0.0048 **
## 3: 7.5 0.6208333 0.7551085 -0.13379284 24 0.012 *
## 4: 10.5 0.6375000 0.7836615 -0.15718140 24 0.0079 **
## 5: 13.5 0.6250000 0.8240112 -0.20576489 24 6.4e-05 ***
## 6: 16.5 0.6375000 0.7818411 -0.15147782 24 0.027 *
## 7: 19.5 0.6541667 0.7263256 -0.07096924 24 0.13 :(
## 8: 22.5 0.6458333 0.7654436 -0.12523757 24 0.046 *
## 9: 25.5 0.6583333 0.7908307 -0.13301969 24 0.0072 **
## 10: 28.5 0.6166667 0.7394768 -0.11097038 24 0.039 *
## time error.diff shapes
## 1: 1.5 -0.23067342 24
## 2: 4.5 -0.16957820 24
## 3: 7.5 -0.13379284 24
## 4: 10.5 -0.15718140 24
## 5: 13.5 -0.20576489 24
## 6: 16.5 -0.15147782 24
## 7: 19.5 -0.07096924 16
## 8: 22.5 -0.12523757 24
## 9: 25.5 -0.13301969 24
## 10: 28.5 -0.11097038 24
##
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff,
## data = DTM[niveau.group == "medium"])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.68934 -0.16575 0.00973 0.19104 0.67014
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.157587 0.040226 3.918 9.92e-05 ***
## timeNorm -0.008747 0.037508 -0.233 0.816
## obj.diff -0.364236 0.054058 -6.738 3.61e-11 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.06744585)
##
## Null deviance: 45.961 on 637 degrees of freedom
## Residual deviance: 42.828 on 635 degrees of freedom
## AIC: 95.236
##
## Number of Fisher Scoring iterations: 2## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.5204545 0.6251419 -0.096882522 44 0.034 *
## 2: 4.5 0.5454545 0.6224524 -0.069888163 66 0.042 *
## 3: 7.5 0.5212121 0.5482212 -0.022392160 66 0.54 :(
## 4: 10.5 0.5257576 0.5744464 -0.036347555 66 0.35 :(
## 5: 13.5 0.5348485 0.5455378 -0.006192686 66 0.85 :(
## 6: 16.5 0.5272727 0.5560045 -0.033252815 66 0.35 :(
## 7: 19.5 0.4712121 0.5704673 -0.107605826 66 0.0013 **
## 8: 22.5 0.4439394 0.5060978 -0.066259279 66 0.063 .
## 9: 25.5 0.4787879 0.4999714 -0.024063555 66 0.54 :(
## 10: 28.5 0.4787879 0.5016324 -0.029290994 66 0.31 :(
## time error.diff shapes
## 1: 1.5 -0.096882522 24
## 2: 4.5 -0.069888163 24
## 3: 7.5 -0.022392160 16
## 4: 10.5 -0.036347555 16
## 5: 13.5 -0.006192686 16
## 6: 16.5 -0.033252815 16
## 7: 19.5 -0.107605826 24
## 8: 22.5 -0.066259279 16
## 9: 25.5 -0.024063555 16
## 10: 28.5 -0.029290994 16
##
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff,
## data = DTM[niveau.group == "good"])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.61934 -0.16018 0.01038 0.17385 0.53652
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.11181 0.02651 4.217 2.78e-05 ***
## timeNorm 0.02800 0.02850 0.983 0.326
## obj.diff -0.19693 0.04083 -4.823 1.71e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.04519554)
##
## Null deviance: 35.197 on 753 degrees of freedom
## Residual deviance: 33.942 on 751 degrees of freedom
## AIC: -190.2
##
## Number of Fisher Scoring iterations: 2## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.5192308 0.5021701 0.020553607 52 0.56 :(
## 2: 4.5 0.4820513 0.4581003 0.028602593 78 0.28 :(
## 3: 7.5 0.4602564 0.4705078 -0.007131425 78 0.76 :(
## 4: 10.5 0.5089744 0.4361551 0.085310624 78 0.0021 **
## 5: 13.5 0.4474359 0.3993679 0.055711847 78 0.043 *
## 6: 16.5 0.4846154 0.4316421 0.056312672 78 0.036 *
## 7: 19.5 0.4717949 0.4386951 0.030866623 78 0.22 :(
## 8: 22.5 0.4551282 0.3910376 0.068335238 78 0.013 *
## 9: 25.5 0.4256410 0.3686849 0.059334781 78 0.014 *
## 10: 28.5 0.4051282 0.3329405 0.069444110 78 0.0055 **
## time error.diff shapes
## 1: 1.5 0.020553607 16
## 2: 4.5 0.028602593 16
## 3: 7.5 -0.007131425 16
## 4: 10.5 0.085310624 24
## 5: 13.5 0.055711847 24
## 6: 16.5 0.056312672 24
## 7: 19.5 0.030866623 16
## 8: 22.5 0.068335238 24
## 9: 25.5 0.059334781 24
## 10: 28.5 0.069444110 24
Per group, sensory task
##
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff,
## data = DTS[niveau.group == "bad"])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.73870 -0.20639 -0.03261 0.20631 0.62395
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.21907 0.04432 4.943 1.31e-06 ***
## timeNorm 0.04070 0.05278 0.771 0.441
## obj.diff -0.51813 0.04443 -11.662 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.06278642)
##
## Null deviance: 26.637 on 289 degrees of freedom
## Residual deviance: 18.020 on 287 degrees of freedom
## AIC: 25.244
##
## Number of Fisher Scoring iterations: 2## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.5200000 0.6406758 -0.14084025 20 0.11 :(
## 2: 4.5 0.5233333 0.6698386 -0.14736319 30 0.023 *
## 3: 7.5 0.5600000 0.7188233 -0.16920919 30 0.0047 **
## 4: 10.5 0.6166667 0.7030218 -0.09216708 30 0.12 :(
## 5: 13.5 0.6300000 0.7358829 -0.09735674 30 0.047 *
## 6: 16.5 0.5033333 0.6317603 -0.17169488 30 0.025 *
## 7: 19.5 0.5666667 0.6733686 -0.14446148 30 0.061 .
## 8: 22.5 0.6766667 0.7281888 -0.04561332 30 0.53 :(
## 9: 25.5 0.5200000 0.6381949 -0.10625786 30 0.07 .
## 10: 28.5 0.5400000 0.6229223 -0.06610116 30 0.28 :(
## time error.diff shapes
## 1: 1.5 -0.14084025 16
## 2: 4.5 -0.14736319 24
## 3: 7.5 -0.16920919 24
## 4: 10.5 -0.09216708 16
## 5: 13.5 -0.09735674 24
## 6: 16.5 -0.17169488 24
## 7: 19.5 -0.14446148 16
## 8: 22.5 -0.04561332 16
## 9: 25.5 -0.10625786 16
## 10: 28.5 -0.06610116 16
##
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff,
## data = DTS[niveau.group == "medium"])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.72177 -0.20599 0.01874 0.20085 0.76816
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.11023 0.02506 4.398 1.24e-05 ***
## timeNorm 0.04083 0.03333 1.225 0.221
## obj.diff -0.31517 0.02602 -12.115 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.06764777)
##
## Null deviance: 62.813 on 782 degrees of freedom
## Residual deviance: 52.765 on 780 degrees of freedom
## AIC: 118.09
##
## Number of Fisher Scoring iterations: 2## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.5185185 0.5861542 -0.078070575 54 0.067 .
## 2: 4.5 0.5839506 0.6542733 -0.051796887 81 0.0091 **
## 3: 7.5 0.4753086 0.4885366 -0.021849678 81 0.5 :(
## 4: 10.5 0.5246914 0.5974452 -0.067630645 81 0.038 *
## 5: 13.5 0.4975309 0.5796225 -0.073998141 81 0.0059 **
## 6: 16.5 0.4765432 0.5246856 -0.047858770 81 0.13 :(
## 7: 19.5 0.5222222 0.5750254 -0.028466125 81 0.2 :(
## 8: 22.5 0.4962963 0.5354099 -0.034256700 81 0.13 :(
## 9: 25.5 0.5827160 0.5863455 -0.002667718 81 0.9 :(
## 10: 28.5 0.5555556 0.5940647 -0.044429134 81 0.14 :(
## time error.diff shapes
## 1: 1.5 -0.078070575 16
## 2: 4.5 -0.051796887 24
## 3: 7.5 -0.021849678 16
## 4: 10.5 -0.067630645 24
## 5: 13.5 -0.073998141 24
## 6: 16.5 -0.047858770 16
## 7: 19.5 -0.028466125 16
## 8: 22.5 -0.034256700 16
## 9: 25.5 -0.002667718 16
## 10: 28.5 -0.044429134 16
##
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff,
## data = DTS[niveau.group == "good"])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.6776 -0.1565 -0.0853 0.2416 0.7578
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.06999 0.02734 2.560 0.0107 *
## timeNorm 0.03858 0.03825 1.009 0.3136
## obj.diff -0.38977 0.02922 -13.340 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.06601407)
##
## Null deviance: 49.907 on 579 degrees of freedom
## Residual deviance: 38.090 on 577 degrees of freedom
## AIC: 74.586
##
## Number of Fisher Scoring iterations: 2## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.3725000 0.5741750 -0.21489529 40 0.00021 ***
## 2: 4.5 0.3933333 0.5163915 -0.10929842 60 0.0015 **
## 3: 7.5 0.4016667 0.4914797 -0.07982177 60 0.026 *
## 4: 10.5 0.4416667 0.4983217 -0.06076145 60 0.051 .
## 5: 13.5 0.3583333 0.4479451 -0.07803043 60 0.014 *
## 6: 16.5 0.3300000 0.4729050 -0.14565076 60 0.00013 ***
## 7: 19.5 0.3933333 0.4908181 -0.08289150 60 0.0029 **
## 8: 22.5 0.4016667 0.5101706 -0.10134460 60 0.01 *
## 9: 25.5 0.4800000 0.5466303 -0.04452465 60 0.13 :(
## 10: 28.5 0.3966667 0.5086757 -0.11133031 60 0.0045 **
## time error.diff shapes
## 1: 1.5 -0.21489529 24
## 2: 4.5 -0.10929842 24
## 3: 7.5 -0.07982177 24
## 4: 10.5 -0.06076145 16
## 5: 13.5 -0.07803043 24
## 6: 16.5 -0.14565076 24
## 7: 19.5 -0.08289150 24
## 8: 22.5 -0.10134460 24
## 9: 25.5 -0.04452465 16
## 10: 28.5 -0.11133031 24
Per group, logical task
##
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff,
## data = DTL[niveau.group == "bad"])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.71478 -0.14986 -0.08709 0.27424 0.49224
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.41766 0.06225 6.709 8.03e-11 ***
## timeNorm 0.10877 0.05400 2.014 0.0447 *
## obj.diff -0.79794 0.06078 -13.128 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.07235681)
##
## Null deviance: 40.087 on 347 degrees of freedom
## Residual deviance: 24.963 on 345 degrees of freedom
## AIC: 78.67
##
## Number of Fisher Scoring iterations: 2## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.5375000 0.8742824 -0.3400032294 24 6e-07 ***
## 2: 4.5 0.5666667 0.8561091 -0.3102717066 36 6.7e-06 ***
## 3: 7.5 0.6500000 0.8206731 -0.1960451679 36 0.0049 **
## 4: 10.5 0.6500000 0.7270999 -0.0819161707 36 0.19 :(
## 5: 13.5 0.6277778 0.7508644 -0.1701449326 36 0.043 *
## 6: 16.5 0.6944444 0.7837756 -0.1034756273 36 0.098 .
## 7: 19.5 0.6694444 0.7431148 -0.0686429014 36 0.098 .
## 8: 22.5 0.5527778 0.6943478 -0.1472475774 36 0.018 *
## 9: 25.5 0.6305556 0.6566567 0.0008706139 36 0.99 :(
## 10: 28.5 0.6638889 0.6385846 0.0191168357 36 0.67 :(
## time error.diff shapes
## 1: 1.5 -0.3400032294 24
## 2: 4.5 -0.3102717066 24
## 3: 7.5 -0.1960451679 24
## 4: 10.5 -0.0819161707 16
## 5: 13.5 -0.1701449326 24
## 6: 16.5 -0.1034756273 16
## 7: 19.5 -0.0686429014 16
## 8: 22.5 -0.1472475774 24
## 9: 25.5 0.0008706139 16
## 10: 28.5 0.0191168357 16## Warning: Removed 2 rows containing missing values (geom_errorbar).
##
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff,
## data = DTL[niveau.group == "medium"])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.61698 -0.09821 -0.02095 0.05412 0.55541
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.423728 0.031809 13.321 <2e-16 ***
## timeNorm 0.003163 0.036698 0.086 0.931
## obj.diff -0.802814 0.032859 -24.432 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.04686494)
##
## Null deviance: 53.857 on 492 degrees of freedom
## Residual deviance: 22.964 on 490 degrees of freedom
## AIC: -104.76
##
## Number of Fisher Scoring iterations: 2## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.4647059 0.6046363 -0.12935792 34 0.016 *
## 2: 4.5 0.5647059 0.7746858 -0.21647631 51 8e-06 ***
## 3: 7.5 0.5470588 0.6001055 -0.07284797 51 0.12 :(
## 4: 10.5 0.5960784 0.5613586 0.01741505 51 0.7 :(
## 5: 13.5 0.5705882 0.5729191 -0.00639387 51 0.84 :(
## 6: 16.5 0.5627451 0.5820839 -0.03055703 51 0.46 :(
## 7: 19.5 0.4862745 0.5010754 -0.01856087 51 0.67 :(
## 8: 22.5 0.5274510 0.4692855 0.06286297 51 0.25 :(
## 9: 25.5 0.5196078 0.4338492 0.08407206 51 0.067 .
## 10: 28.5 0.4666667 0.3688026 0.10634997 51 0.045 *
## time error.diff shapes
## 1: 1.5 -0.12935792 24
## 2: 4.5 -0.21647631 24
## 3: 7.5 -0.07284797 16
## 4: 10.5 0.01741505 16
## 5: 13.5 -0.00639387 16
## 6: 16.5 -0.03055703 16
## 7: 19.5 -0.01856087 16
## 8: 22.5 0.06286297 16
## 9: 25.5 0.08407206 16
## 10: 28.5 0.10634997 24
##
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff,
## data = DTL[niveau.group == "good"])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.64640 -0.21722 -0.01476 0.20979 0.70507
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.14926 0.02655 5.623 2.59e-08 ***
## timeNorm 0.07344 0.03468 2.118 0.0345 *
## obj.diff -0.42494 0.03359 -12.652 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.06759368)
##
## Null deviance: 68.529 on 811 degrees of freedom
## Residual deviance: 54.683 on 809 degrees of freedom
## AIC: 121.63
##
## Number of Fisher Scoring iterations: 2## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.3857143 0.4886803 -0.106804595 56 0.042 *
## 2: 4.5 0.4488095 0.4741611 -0.035984911 84 0.33 :(
## 3: 7.5 0.4261905 0.4308157 -0.008905831 84 0.78 :(
## 4: 10.5 0.4190476 0.4327296 -0.012280159 84 0.73 :(
## 5: 13.5 0.4345238 0.3887083 0.053471734 84 0.2 :(
## 6: 16.5 0.3976190 0.3279099 0.068597536 84 0.045 *
## 7: 19.5 0.3571429 0.2728660 0.079800480 84 0.029 *
## 8: 22.5 0.3142857 0.2463567 0.051170859 84 0.11 :(
## 9: 25.5 0.3535714 0.2412372 0.115593592 84 0.0023 **
## 10: 28.5 0.3452381 0.2192474 0.118100349 84 0.0045 **
## time error.diff shapes
## 1: 1.5 -0.106804595 24
## 2: 4.5 -0.035984911 16
## 3: 7.5 -0.008905831 16
## 4: 10.5 -0.012280159 16
## 5: 13.5 0.053471734 16
## 6: 16.5 0.068597536 24
## 7: 19.5 0.079800480 24
## 8: 22.5 0.051170859 16
## 9: 25.5 0.115593592 24
## 10: 28.5 0.118100349 24
Link between d(t-1) and error
## Warning: Removed 28 rows containing missing values (geom_path).## geom_path: Each group consists of only one observation. Do you need to
## adjust the group aesthetic?
## Warning: Removed 20 rows containing missing values (geom_path).
## Warning: Removed 9 rows containing missing values (geom_path).
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 4.3066, p-value = 1.657e-05
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.07215095
##
## [1] "DTM 0.072 1.7e-05 ***"## [1] "0.072 1.7e-05 ***"## Warning: Removed 28 rows containing missing values (geom_path).## geom_path: Each group consists of only one observation. Do you need to
## adjust the group aesthetic?
## Warning: Removed 26 rows containing missing values (geom_path).
## Warning: Removed 4 rows containing missing values (geom_path).
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 1.6632, p-value = 0.09627
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.02774026
##
## [1] "DTS 0.028 0.096 ."## [1] "0.028 0.096 ."## Warning: Removed 28 rows containing missing values (geom_path).## geom_path: Each group consists of only one observation. Do you need to
## adjust the group aesthetic?
## Warning: Removed 26 rows containing missing values (geom_path).
## Warning: Removed 17 rows containing missing values (geom_path).
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 1.7993, p-value = 0.07197
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.03026999
##
## [1] "DTL 0.03 0.072 ."## [1] "0.03 0.072 ."Subjective difficulty and play habits
Playing video game in general and subjective difficulty error
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = -0.53848, p-value = 0.5902
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## -0.03211663Playing board game in general and subjective difficulty error
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = -2.2508, p-value = 0.0244
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## -0.1324307
##
## [1] "pbg.on.error -0.13 0.024 *"In game level and subjective difficulty error
## [1] "niveau.group.on.error 0.055 ."
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 2.1336, p-value = 0.03287
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.1108407
##
## [1] "niveau.group.on.error 0.11 0.033 *"
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 0.72089, p-value = 0.471
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.06623377
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 1.2253, p-value = 0.2205
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.1115288
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 1.7072, p-value = 0.08779
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.1553885
##
## [1] "niveau.group.on.error.l 0.16 0.088 ."Sex and subjective difficulty error
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 2.73, p-value = 0.006333
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.1729497
##
## [1] "sexe.on.error 0.17 0.0063 **"
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 1.5088, p-value = 0.1314
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.1675869
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 1.6759, p-value = 0.09376
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.1844506
##
## [1] "sexe.on.error.s 0.18 0.094 ."
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 1.5107, p-value = 0.1309
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.1662753
##
## Wilcoxon rank sum test with continuity correction
##
## data: B and A
## W = 3982, p-value = 0.006365
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## 0.01443072 0.07703311
## sample estimates:
## difference in location
## 0.04013073
##
## [1] "sexe.on.error.2 0.04 0.0064 ** mean(A): -0.05 mean(B): -0.0069"
##
## Wilcoxon rank sum test
##
## data: B and A
## W = 428, p-value = 0.1346
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -0.01093459 0.09925703
## sample estimates:
## difference in location
## 0.03800372
##
## Wilcoxon rank sum test
##
## data: B and A
## W = 460, p-value = 0.09567
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -0.006887625 0.100722013
## sample estimates:
## difference in location
## 0.04302402
##
## [1] "sexe.on.error.s.2 0.043 0.096 . mean(A): -0.049 mean(B): -0.0036"
##
## Wilcoxon rank sum test
##
## data: B and A
## W = 439, p-value = 0.134
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -0.01099769 0.09666576
## sample estimates:
## difference in location
## 0.03838581Risk aversion and subjective difficulty error
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 0.84928, p-value = 0.3957
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.04790825
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 0.21777, p-value = 0.8276
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.02158799
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 0.51597, p-value = 0.6059
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.0505826
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 0.73613, p-value = 0.4617
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.07231267Self efficacy and subjective difficulty error
## Warning: Removed 84 rows containing missing values (geom_point).
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = -2.6664, p-value = 0.007666
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## -0.2037486
##
## [1] "self.eff.on.error -0.2 0.0077 **"## Warning in cor.test.default(Y, X, method = "kendall"): Cannot compute exact
## p-value with ties## Warning: Removed 28 rows containing missing values (geom_point).
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = -1.4877, p-value = 0.1368
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## -0.2024706## Warning in cor.test.default(Y, X, method = "kendall"): Cannot compute exact
## p-value with ties
## Warning in cor.test.default(Y, X, method = "kendall"): Removed 28 rows
## containing missing values (geom_point).
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = -1.527, p-value = 0.1268
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## -0.2043462## Warning in cor.test.default(Y, X, method = "kendall"): Cannot compute exact
## p-value with ties
## Warning in cor.test.default(Y, X, method = "kendall"): Removed 28 rows
## containing missing values (geom_point).
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = -1.527, p-value = 0.1268
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## -0.2043462